A soft tissue displacement prediction method for skeletal class iii malocclusion orthodontic and orthognathic

Abstract

The present application relates to the technical field of facial soft tissue displacement prediction, and discloses a soft tissue displacement prediction method for skeletal class III malocclusion orthodontic and orthognathic surgery, comprising the following steps: constructing STL format bone tissue and soft tissue three-dimensional entity models before and after orthodontic and orthognathic surgery based on CBCT data of a skeletal class III malocclusion patient before and after orthodontic and orthognathic surgery; establishing an oral cavity jaw face standard coordinate system and obtaining coordinates of the STL format bone tissue and soft tissue three-dimensional entity models before and after orthodontic and orthognathic surgery; obtaining 3dMD facial scanning files before and after orthodontic and orthognathic surgery of the skeletal class III malocclusion patient, and registering the 3dMD facial scanning files with landmark points of the bone tissue and soft tissue three-dimensional entity models to obtain a registration model in which soft tissue in the three-dimensional entity model is replaced by the 3dMD facial scanning file; and based on the registration model, a soft tissue displacement is predicted by using a stepwise regression method. The present application improves the prediction accuracy of facial soft tissue displacement before and after skeletal class III malocclusion orthodontic and orthognathic surgery.

Description

Technical Field

[0001] This invention relates to the field of facial soft tissue displacement prediction technology, and more specifically, to a method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion. Background Technology

[0002] Class III skeletal malocclusion is a relatively common jaw deformity in clinical practice. Patients exhibit a distinctly concave facial profile, sagittal disharmony, mesial relationship of molars, anterior crossbite, and anterior lateral occlusion (ANB) < 0°. This condition not only affects the patient's biting, chewing, and speech functions but also significantly impacts facial aesthetics, and in severe cases, can even affect the patient's mental health and social activities. Combined orthodontic and orthognathic surgery is currently the main clinical treatment for Class III skeletal malocclusion. The treatment process consists of preoperative orthodontics, orthognathic surgery, and postoperative orthodontics. Preoperative orthodontics includes aligning teeth and adjusting arch width. Orthognathic surgery includes a maxillary Lessor I osteotomy combined with bilateral mandibular ramus sagittal split osteotomy. Postoperative orthodontics finely adjusts the bite, modifying the horizontal, vertical, and anteroposterior relationships of the maxilla and mandible, as well as tooth alignment, thereby improving the patient's facial aesthetics.

[0003] Before performing orthognathic surgery, doctors first need to determine the position of the soft tissues that will result in a harmonious facial profile with good nasolabial-chin coordination after surgery. Then, they calculate the amount of hard tissue movement based on this. However, there has always been a problem between the actual ratio of soft and hard tissue movement after a Class III bony surgery and the theoretical amount of movement designed in the preoperative plan: due to the lack of a precise correlation between the movement of soft and hard tissues, it is impossible to accurately achieve the amount of hard tissue movement required for the nasolabial-chin soft tissues to achieve a harmonious aesthetic relationship.

[0004] Although there are a few international reports on changes in the soft and hard tissues of the nasolabial region after orthognathic surgery, many studies on tissue prediction before and after skeletal Class III orthognathic surgery analyze soft and hard tissue changes independently. Furthermore, other studies rely solely on CT data for measurement, which presents the following problems: Color information in the maxillofacial region, especially the nasolabial region, plays a crucial role in accurately locating landmarks such as the cupid's bow and corner of the mouth. However, CT data lacks facial color information, leading to decreased accuracy in landmark location and affecting the accuracy of soft tissue displacement measurement. Additionally, because patients are in a supine position during CT scans, the soft tissues of the cheeks undergo slight deformation under gravity, causing discrepancies between the pre-operatively planned bone movement and the actual post-operative soft tissue feedback, thus reducing the reliability of soft tissue displacement prediction.

[0005] With the development of imaging processing technology, measurement techniques for analyzing facial appearance have evolved from two-dimensional to three-dimensional. 3dMD (3D computed tomography) has been widely used in soft tissue research of the maxillofacial region in recent years, and its accuracy and reliability in analyzing craniofacial morphology have been preliminarily confirmed. Some researchers have used CBCT to reconstruct patient soft tissue data and matched it with 3dMD facial scan data to measure changes in various regions of the patient's soft tissue. However, they failed to measure the data before and after orthodontic treatment in the same coordinate system, resulting in limited measurement accuracy. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for predicting soft tissue displacement in orthodontic treatment for skeletal Class III malocclusion. It simulates the fitting of soft and hard tissues before and after orthodontic treatment in the same spatial coordinate system and combines it with 3dMD facial scan color data to significantly improve the prediction accuracy of facial soft tissue displacement before and after orthodontic treatment for skeletal Class III malocclusion.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0008] A method for predicting soft tissue displacement in orthognathic surgery for skeletal Class III malocclusion includes the following steps: Step S1: Based on the CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment, construct three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic treatment. Step S2: Establish a standard coordinate system for the oral and maxillofacial region, obtain the coordinates of the three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic treatment, and import them into 3-matic 21.0; Step S3: Obtain 3dMD facial scan files of patients with skeletal Class III malocclusion before and after orthodontic treatment, import them into 3-matic 21.0 in OBJ format, and register them with the coordinates of the landmark points of the 3D solid models of bone and soft tissue in the imported 3-matic 21.0 to obtain a registration model that replaces the soft tissue in the 3D solid model with the 3dMD facial scan file. Step S4: Based on the registration model, the stepwise regression method is used to predict soft tissue displacement.

[0009] Further, step S1 includes the following sub-steps: Step S1.1: Import the CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment into the biomedical engineering software Mimics 21.0 in DICOM format; Step S1.2: Using the threshold segmentation function of the biomedical engineering software Mimics 21.0, adjust the HU value of the CBCT data to extract bone and soft tissue from the CBCT data; Step S1.3: Use the computational solid model of biomedical engineering software Mimics 21.0 to create a preview 3D model of bone and soft tissue, and use the lasso and smoothing tools in the editing mask to refine it, generating STL format 3D solid models of bone and soft tissue before and after orthodontic orthognathic surgery.

[0010] Furthermore: For bone tissue, the HU value of CBCT data was adjusted to 525~3187; For soft tissue, the HU value of CBCT data was adjusted to -383 to 489.

[0011] Furthermore, the specific process of step S2 is as follows: The three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were imported into Creo Parametric 7.0. A horizontal plane was established through the midpoint between the bilateral infraorbital margins and the upper point of the external auditory canal. A sagittal plane was established through the midpoint of the foramen magnum, the root of the nose, and the vertical horizontal plane. A coronal plane was established through the midpoint of the external auditory canal and parallel to the horizontal and sagittal planes. The three-dimensional solid models in the standard coordinate system of the oral and maxillofacial region were obtained. The files of the three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were updated and imported into 3-matic 21.0.

[0012] Furthermore, step S3 includes the following sub-steps: Step S3.1: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion after orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model after orthodontic treatment in 3-matic 21.0, and set the bone tissue in the 3D solid model after orthodontic treatment to follow the state. Trace the first set of landmark points and register the 3dMD facial scan file after orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function until the registration result between the 3dMD facial scan file after orthodontic treatment and the 3D solid model after orthodontic treatment no longer changes, and obtain the registration result after orthodontic treatment. Step S3.2: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion before orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model of orthodontic treatment before orthodontic treatment in 3-matic 21.0. Trace the first set of landmark points and register the 3dMD facial scan file before orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function until the registration result of the 3dMD facial scan file before orthodontic treatment before orthodontic treatment and the 3D solid model of orthodontic treatment before orthodontic treatment no longer changes, and obtain the registration result before orthodontic treatment. Step S3.3: Using the N-point fusion function in the Align function, fix the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result, set the 3dMD facial scan file in the orthodontic orthognathic post-registration result to follow state, trace the second set of landmark points, and register the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result with the bone tissue in the 3D solid model of the orthodontic orthognathic post-registration result.

[0013] Furthermore, the first set of landmarks includes: bilateral medial canthi points, bilateral lateral canthi points, nasal tip point, bilateral nasal alar base points, and bilateral corner points of the mouth; the second set of landmarks includes: nasal root point, bilateral infraorbital margin points, bilateral infraorbital foramina, and midpoint of the foramen magnum.

[0014] Furthermore, the predicted soft tissue displacements in step S4 include: displacement of key soft tissue landmarks in the maxilla, displacement of key soft tissue landmarks in the mandible, and displacement of key soft tissue landmarks in the chin.

[0015] Furthermore, the prediction process for the displacement of key landmarks in the maxillary soft tissue is as follows:

[0016] in, This represents the predicted displacement value of the upper lip notch in the maxillary soft tissue. This represents the displacement value of the upper tooth seat point in the registration model. This represents the nasolabial angle value in the 3D solid model before orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the first constant term.

[0017] Furthermore, the prediction process for the displacement of key landmarks in the mandibular soft tissue is as follows:

[0018] in, This represents the predicted displacement value of the lower lip notch in the mandibular soft tissue. This represents the displacement value of the lower tooth seat point in the registration model. This represents the thickness at the midpoint of the mentolabial sulcus in the three-dimensional solid model before orthodontic treatment. These represent the coefficients of the corresponding terms. This represents the second constant term.

[0019] Furthermore, the prediction process for the displacement of key landmarks in the chin soft tissue is as follows:

[0020] in, This represents the predicted displacement value of the premental point in the soft tissue of the chin. This represents the displacement value of the anterior chin point in the bone tissue during the registration model. This indicates the numerical value of the anterior lip-mental angle in orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the third constant term.

[0021] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for predicting soft tissue displacement during orthodontic treatment of skeletal Class III malocclusion. After establishing a standard coordinate system for the oral and maxillofacial region, the coordinates of the STL format 3D solid models of bone and soft tissue before and after orthodontic treatment are updated to the same coordinate system. This avoids calculation deviations in soft and hard tissue displacement caused by inconsistent benchmarks at different stages. Simultaneously, 3dMD facial scan files are registered with 3D solid models of soft tissue generated from CBCT data in 3-matic 21.0. Because the 3dMD facial scan files contain color and 3D stereoscopic information, the spatial mapping of soft and bone tissues is more accurate. The predicted soft tissue displacement from the 3D solid models before and after orthodontic treatment, registered in the same coordinate system, more closely approximates the actual facial changes after orthodontic treatment, significantly improving the prediction accuracy of soft tissue displacement before and after orthodontic treatment for skeletal Class III malocclusion. Attached Figure Description

[0022] Figure 1 This is a flowchart of the soft tissue displacement prediction method for orthognathic surgery for skeletal Class III malocclusion according to the present invention. Detailed Implementation

[0023] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings.

[0024] like Figure 1 This is a flowchart of the soft tissue displacement prediction method for orthodontic treatment of skeletal Class III malocclusion according to the present invention. The soft tissue displacement prediction method includes the following steps: Step S1: Based on CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment, construct three-dimensional solid models of bone and soft tissue in STL format for both procedures; including the following sub-steps: Step S1.1: Import the CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment into the biomedical engineering software Mimics 21.0 in DICOM format; Step S1.2: Using the threshold segmentation function of the biomedical engineering software Mimics 21.0, adjust the HU value of the CBCT data to extract bone and soft tissue from the CBCT data; for bone tissue, make the contours of the anterior nasal spine and the midpoint of the incisors clearly visible, while preventing artifacts such as metal brackets and intraoperative implanted titanium screws from excessively affecting the measured HU range. Therefore, adjust the HU value of the CBCT data to 525~3187; for soft tissue, make the nasolabial contour continuous and smooth, while removing the influence of instrument-assisted benchmarks to the greatest extent, adjust the HU value of the CBCT data to -383~489.

[0025] Step S1.3: Use the computational solid model of biomedical engineering software Mimics 21.0 to create a preview 3D model of bone and soft tissue, and use the lasso tool in the editing mask to remove redundant structures, and use the smoothing tool to drag and refine the model surface to generate STL format 3D solid models of bone and soft tissue before and after orthodontic treatment.

[0026] Step S2: Establish a standard coordinate system for the oral and maxillofacial region, overriding the default random origin coordinate system of CBCT data. Obtain the coordinates of the 3D solid models of bone and soft tissue in STL format before and after orthodontic treatment, and import them into 3-matic21.0 to avoid calculation deviations in soft tissue displacement caused by inconsistent benchmarks at different stages. Specifically: The three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were imported into Creo Parametric 7.0. A horizontal plane was established through the midpoint between the bilateral infraorbital margins and the upper point of the external auditory canal. A sagittal plane was established through the midpoint of the foramen magnum, the root of the nose, and the vertical horizontal plane. A coronal plane was established through the midpoint of the external auditory canal and parallel to the horizontal and sagittal planes. The three-dimensional solid models in the standard coordinate system of the oral and maxillofacial region were obtained. The files of the three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were updated and saved, and then imported into 3-matic 21.0.

[0027] Step S3: Obtain 3dMD facial scan files of patients with skeletal Class III malocclusion before and after orthodontic treatment. Import these files into 3-matic 21.0 in OBJ format and register them with the landmark coordinates of the bone and soft tissue 3D solid models imported into 3-matic 21.0. This results in a registration model that replaces the soft tissue in the 3D solid model with the 3dMD facial scan file. Because the 3dMD facial scan file contains color and 3D stereoscopic information, the spatial mapping of soft and bone tissues is more accurate. This includes the following sub-steps: Step S3.1: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion after orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model after orthodontic treatment in 3-matic 21.0, and set the bone tissue in the 3D solid model after orthodontic treatment to follow the state so as not to interfere with the synchronization of soft tissue and bone tissue in the CBCT data of the patient with skeletal Class III malocclusion after orthodontic treatment. Trace the first set of landmark points and register the 3dMD facial scan file after orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function to further reduce matching error until the registration result of the 3dMD facial scan file after orthodontic treatment and the 3D solid model after orthodontic treatment no longer changes, and obtain the registration result after orthodontic treatment. Step S3.2: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion before orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model of orthodontic treatment before orthodontic treatment in 3-matic 21.0. Trace the first set of landmark points and register the 3dMD facial scan file before orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function until the registration result of the 3dMD facial scan file before orthodontic treatment before orthodontic treatment and the 3D solid model of orthodontic treatment before orthodontic treatment no longer changes, and obtain the registration result before orthodontic treatment. Step S3.3: Since the midface and lower face are both orthodontic and orthognathic areas, they do not have fitting conditions. Therefore, the fitting points for bone tissue are all taken from the upper face and the occipital region. The N-point fusion function in Align is used to fix the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result. The 3dMD facial scan file in the orthodontic orthognathic post-registration result is set to follow state, which can directly make the 3dMD facial scan file and bone tissue matched in step S3.1 move synchronously in a combined form. The second set of marker points is traced to register the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result with the bone tissue in the 3D solid model of the orthodontic orthognathic post-registration result.

[0028] The first set of landmarks includes: bilateral medial canthi points, bilateral lateral canthi points, nasal tip point, bilateral nasal alar base points, and bilateral corner points of the mouth; the second set of landmarks includes: nasal root point, bilateral infraorbital margin points, bilateral infraorbital foramina, and midpoint of the foramen magnum.

[0029] Step S4: Based on the bone tissue displacement, the registration model is used to predict the soft tissue displacement using stepwise regression, including the displacement of key soft tissue landmarks in the maxilla, mandible, and chin. This makes the prediction results closer to the actual changes in facial appearance after orthodontic treatment. Furthermore, by using the stepwise regression method to select independent variables in the regression equation, the personalized facial features of different individuals are also taken into account to influence the prediction, significantly improving the prediction accuracy of soft tissue displacement of the face before and after orthodontic treatment for skeletal Class III malocclusion.

[0030] The prediction process for the displacement of key landmarks in the maxillary soft tissue in this invention is as follows:

[0031] in, This represents the predicted displacement value of the upper lip notch in the maxillary soft tissue. This represents the displacement value of the upper tooth seat point in the registration model. This represents the nasolabial angle value in the 3D solid model before orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the first constant term.

[0032] In one technical solution of the present invention, The overall p-value was 0.0424, which is less than the significance threshold of 0.05, indicating that... The calculation was statistically highly significant, with a coefficient of determination of [value missing]. The value of 0.4157 indicates that its model fit in clinical practice is at a relatively good level.

[0033] The prediction process for the displacement of key landmarks in the mandibular soft tissue in this invention is as follows:

[0034] in, This represents the predicted displacement value of the lower lip notch in the mandibular soft tissue. This represents the displacement value of the lower tooth seat point in the registration model. This represents the numerical value of the thickness at the midpoint of the mentolabial sulcus in the three-dimensional solid model before orthodontic treatment. These represent the coefficients of the corresponding terms. This represents the second constant term.

[0035] In one technical solution of the present invention, The overall p-value was 0.0046, which is less than the significance threshold of 0.05, indicating that... The calculation was statistically highly significant, with a coefficient of determination of [value missing]. The value of 0.5475 indicates that its model fit in clinical practice is at a relatively good level.

[0036] The prediction process for the displacement of key landmarks in the chin soft tissue in this invention is as follows:

[0037] in, This represents the predicted displacement value of the premental point in the soft tissue of the chin. This represents the displacement value of the anterior chin point in the bone tissue during the registration model. This indicates the numerical value of the anterior lip-mental angle in orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the third constant term.

[0038] In one technical solution of the present invention, The overall p-value was 0.0225, which is less than the significance threshold of 0.05, indicating that... The calculation was statistically highly significant, with a coefficient of determination of [value missing]. The value of 0.5062 indicates that its model fit in clinical practice is at a relatively good level.

[0039] This invention provides a method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion. Based on the standard oral and maxillofacial coordinate system, it updates the coordinates of the STL format 3D solid models of bone and soft tissue before and after orthodontic treatment to the same coordinate system, avoiding calculation deviations in soft tissue displacement caused by inconsistent benchmarks at different stages. Simultaneously, it registers 3dMD facial scan files with the 3D solid models of bone and soft tissue generated from CBCT data in 3-matic 21.0. Since the 3dMD facial scan files contain color and 3D stereoscopic information, the spatial mapping of soft and bone tissues is more accurate. This allows for the prediction of soft tissue displacement using the 3D solid models registered with bone tissue before and after orthodontic treatment, making the prediction results closer to the actual facial changes after orthodontic treatment and significantly improving the prediction accuracy of soft tissue displacement before and after orthodontic treatment for skeletal Class III malocclusion.

[0040] In one technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program that enables a computer to execute the soft tissue displacement prediction method of the present invention for orthognathic surgery for skeletal Class III malocclusion.

[0041] In one technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the soft tissue displacement prediction method of the present invention for orthognathic surgery for skeletal Class III malocclusion.

[0042] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disc read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0043] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion, characterized in that, Includes the following steps: Step S1: Based on the CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment, construct three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic treatment. Step S2: Establish a standard coordinate system for the oral and maxillofacial region, obtain the coordinates of the three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic treatment, and import them into 3-matic 21.0; Step S3: Obtain 3dMD facial scan files of patients with skeletal Class III malocclusion before and after orthodontic treatment, import them into 3-matic 21.0 in OBJ format, and register them with the coordinates of the landmark points of the 3D solid models of bone and soft tissue in the imported 3-matic 21.0 to obtain a registration model that replaces the soft tissue in the 3D solid model with the 3dMD facial scan file. Step S4: Based on the registration model, the stepwise regression method is used to predict soft tissue displacement.

2. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S1.1: Import the CBCT data of patients with skeletal Class III malocclusion before and after orthodontic treatment into the biomedical engineering software Mimics 21.0 in DICOM format; Step S1.2: Using the threshold segmentation function of the biomedical engineering software Mimics 21.0, adjust the HU value of the CBCT data to extract bone and soft tissue from the CBCT data; Step S1.3: Use the computational solid model of biomedical engineering software Mimics 21.0 to create a preview 3D model of bone and soft tissue, and use the lasso and smoothing tools in the editing mask to refine it, generating STL format 3D solid models of bone and soft tissue before and after orthodontic orthognathic surgery.

3. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 2, characterized in that: For bone tissue, the HU value of CBCT data was adjusted to 525~3187; For soft tissue, the HU value of CBCT data was adjusted to -383 to 489.

4. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 1, characterized in that, The specific process of step S2 is as follows: The three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were imported into CreoParametric 7.

0. A horizontal plane was established through the midpoint between the bilateral infraorbital margins and the upper point of the external auditory canal. A sagittal plane was established through the midpoint of the foramen magnum, the root of the nose, and the vertical horizontal plane. A coronal plane was established through the midpoint of the external auditory canal and parallel to the horizontal and sagittal planes. The three-dimensional solid models in the standard coordinate system of the oral and maxillofacial region were obtained. The files of the three-dimensional solid models of bone and soft tissue in STL format before and after orthodontic orthognathic surgery were updated and imported into 3-matic 21.

0.

5. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S3.1: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion after orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model after orthodontic treatment in 3-matic 21.0, and set the bone tissue in the 3D solid model after orthodontic treatment to follow the state. Trace the first set of landmark points and register the 3dMD facial scan file after orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function until the registration result between the 3dMD facial scan file after orthodontic treatment and the 3D solid model after orthodontic treatment no longer changes, and obtain the registration result after orthodontic treatment. Step S3.2: Import the 3dMD facial scan file of the patient with skeletal Class III malocclusion before orthodontic treatment into 3-matic 21.0 in OBJ format. Use the N-point fusion function in Align to fix the soft tissue in the imported 3D solid model of orthodontic treatment before orthodontic treatment in 3-matic 21.

0. Trace the first set of landmark points and register the 3dMD facial scan file before orthodontic treatment with the fixed soft tissue. Switch to global fusion in Align function until the registration result of the 3dMD facial scan file before orthodontic treatment before orthodontic treatment and the 3D solid model of orthodontic treatment before orthodontic treatment no longer changes, and obtain the registration result before orthodontic treatment. Step S3.3: Using the N-point fusion function in the Align function, fix the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result, set the 3dMD facial scan file in the orthodontic orthognathic post-registration result to follow state, trace the second set of landmark points, and register the bone tissue in the 3D solid model of the orthodontic orthognathic pre-registration result with the bone tissue in the 3D solid model of the orthodontic orthognathic post-registration result.

6. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 5, characterized in that, The first set of landmarks includes: bilateral medial canthi points, bilateral lateral canthi points, nasal tip point, bilateral nasal alar base points, and bilateral corner points of the mouth; the second set of landmarks includes: nasal root point, bilateral infraorbital margin points, bilateral infraorbital foramina, and midpoint of the foramen magnum.

7. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 1, characterized in that, The predicted soft tissue displacements in step S4 include: displacement of key soft tissue landmarks in the maxilla, displacement of key soft tissue landmarks in the mandible, and displacement of key soft tissue landmarks in the chin.

8. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 7, characterized in that, The prediction process for the displacement of key landmarks in the maxillary soft tissue is as follows: in, This represents the predicted displacement value of the upper lip notch in the maxillary soft tissue. This represents the displacement value of the upper tooth seat point in the registration model. This represents the nasolabial angle value in the 3D solid model before orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the first constant term.

9. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 7, characterized in that, The prediction process for the displacement of key landmarks in the mandibular soft tissue is as follows: in, This represents the predicted displacement value of the lower lip notch in the mandibular soft tissue. This represents the displacement value of the lower tooth seat point in the registration model. This represents the thickness at the midpoint of the mentolabial sulcus in the three-dimensional solid model before orthodontic treatment. These represent the coefficients of the corresponding terms. This represents the second constant term.

10. The method for predicting soft tissue displacement in orthodontic treatment of skeletal Class III malocclusion according to claim 7, characterized in that, The prediction process for the displacement of key landmarks in the chin soft tissue is as follows: in, This represents the predicted displacement value of the premental point in the soft tissue of the chin. This represents the displacement value of the anterior chin point in the bone tissue during the registration model. This indicates the numerical value of the anterior lip-mental angle in orthognathic surgery. These represent the coefficients of the corresponding terms. This represents the third constant term.

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